Top-down arbitrary-shape scene text detectors: These detectors either carry out the binary segmentation or regress key contour points based on proposals.For the segmentation-based methods, they usually perform the pixel-wise semantic segmentation for all pixels in the proposals, inspried by the framework of MaskRCNN [1]}, and mainly focus on enriching the feature representations or obtaining better segmentation [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}. For example, MS-CAFA [7]} exploits a pyramid ROI pooling attention mechanism to learn robust features for propoals with various scales. Mask-TTD [8]} adopts a tightness prior to adjust text proposals for better covering the entire text region, and utilizes the text frontier information to improve the text mask prediction.Moreover, in ContourNet [11]}, the authors only perform the segmentation for text contours on the adaptive proposals. For the regression-based methods [12]}, [13]}, [14]} , they directly or dynamically regress the key points on text contours. For example, CTD-CLOC [12]} predicts the offsets of key points to the top-left points, and utilizes the Long Short-Term Memory (LSTM) to smooth the offsets.Instead of the static regression, ATRR [14]} adaptively outputs the point pair using LSTM.
